Experian Data Quality surveyed more than 1,200 professionals from seven countries to drill down on international corporate data management practices.

The research shows that the majority of organizations do have a strategy in place to manage data quality. More significantly, it found that 84 percent plan to invest in additional data quality technology in the next 12 months.

That’s huge for data quality, an area where practitioners were often the lone voice in the wilderness, trying to win over business users and leaders on the benefits of better data.

So what changed? The pain points have grown for organizations, which now want to leverage BI and analytics tools for data-driven decisions. It’s hard to do that when you can’t trust the data. U.S. businesses estimate that 32 percent of their information is inaccurate, the report found.

“Eighty-three percent of respondents in commercial companies believe their revenue is affected by inaccurate and incomplete customer or prospect data in terms of wasted resources, lost productivity or wasted marketing and communications spend,” the report notes.

While organizations are investing in data quality solutions, the report warns that only one in four organizations has a sophisticated, optimized approach to data quality. One reason is that data quality is still seen as a departmental problem, rather than requiring an enterprise-wide approach. That actually might not be as bad as the report makes it seem, since you want business ownership of data quality.

The report also includes insights about what companies are doing now. While it sounds impressive to say that 88 percent have implemented some sort of data quality solution, it gets a bit less impressive when you see the numbers for data quality tools. For instance:

Only 33 percent have invested in data cleansing tools.

43 percent have invested in profiling tools.

42 percent have invested in monitoring and auditing.

That may be why a surprising 64 percent say they’ll focus on a new data quality solution in 2015. Hopefully, while they’re at it, they’ll look beyond technology to a data quality strategy.

What’s driving data quality has also changed in recent years. While cost savings and fraud have traditionally driven data quality efforts, companies are now more concerned about how data quality affects decision-making and customer satisfaction. That makes sense, when you think about the push in recent years to expand BI and analytics, as well as the drive to extract more value from expensive enterprise applications, such as CRM.

Experian recommends that organizations take four foundational steps to build a more sophisticated data management strategy:

You can centralize data management in various ways. For instance, you could establish a data governance team that would oversee data quality. The company can hire or appoint a chief data officer. BI expert Howard Dresner recommends forming a BI Competency Center that can help “bridge the gap when there is conflicting data among departments or business units.”

Regardless of how you achieve it, centralization is a smart step since more organizations use data as an enterprise-wide resource. That said, if your organization is still in the dark ages over data quality, there’s an argument for de-centralized data quality as the best bet.

Loraine Lawson is a veteran technology reporter and blogger. She currently writes the Integration blog for IT Business Edge, which covers all aspects of integration technology, including data governance and best practices. She has also covered IT/Business Alignment and IT Security for IT Business Edge. Before becoming a freelance writer, Lawson worked at TechRepublic as a site editor and writer, covering mobile, IT management, IT security and other technology trends. Previously, she was a webmaster at the Kentucky Transportation Cabinet and a newspaper journalist. Follow Lawson at Google+ and on Twitter.

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And cleaning up your customer data is only the start of the journey, wait until these companies look under the covers of their other data domains (Product, Location, Content, Material etc.) and realize the potential negative impacts of not having a holistic data governance/quality practice in place. Reply

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